A Generalized Markov-Chain Modelling Approach to (1,λ)(1,\lambda)-ES Linear Optimization: Technical Report

Several recent publications investigated Markov-chain modelling of linear optimization by a $(1,\lambda)$-ES, considering both unconstrained and linearly constrained optimization, and both constant and varying step size. All of them assume normality of the involved random steps, and while this is consistent with a black-box scenario, information on the function to be optimized (e.g. separability) may be exploited by the use of another distribution. The objective of our contribution is to complement previous studies realized with normal steps, and to give sufficient conditions on the distribution of the random steps for the success of a constant step-size $(1,\lambda)$-ES on the simple problem of a linear function with a linear constraint. The decomposition of a multidimensional distribution into its marginals and the copula combining them is applied to the new distributional assumptions, particular attention being paid to distributions with Archimedean copulas

Hierarchical Archimedean Copulas for MATLAB and Octave: The HACopula Toolbox

To extend the current implementation of copulas in MATLAB to non-elliptical distributions in arbitrary dimensions enabling for asymmetries in the tails, the toolbox HACopula provides functionality for modeling with hierarchical (or nested) Archimedean copulas. This includes their representation as MATLAB objects, evaluation, sampling, estimation and goodness-of-fit testing, as well as tools for their visual representation or computation of corresponding matrices of Kendall's tau and tail dependence coefficients. These are first presented in a quick-and-simple manner and then elaborated in more detail to show the full capability of HACopula. As an example, sampling, estimation and goodness-of-fit of a 100-dimensional hierarchical Archimedean copula is presented, including a speed up of its computationally most demanding part. The toolbox is also compatible with Octave, where no support for copulas in more than two dimensions is currently provided

Smart Design of Cz-Ge Crystal Growth Furnace and Process

The aim of this study was to evaluate the potential of the machine learning technique of decision trees to understand the relationships among furnace design, process parameters, crystal quality, and yield in the case of the Czochralski growth of germanium. The ultimate goal was to provide the range of optimal values of 13 input parameters and the ranking of their importance in relation to their impact on three output parameters relevant to process economy and crystal quality. Training data were provided by CFD modelling. The variety of data was ensured by the Design of Experiments method. The results showed that the process parameters, particularly the pulling rate, had a substantially greater impact on the crystal quality and yield than the design parameters of the furnace hot zone. Of the latter, only the crucible size, the axial position of the side heater, and the material properties of the radiation shield were relevant

Telemedicine coverage for post-operative ICU patients.

Introduction There is an increased demand for intensive care unit (ICU) beds. We sought to determine if we could create a safe surge capacity model to increase ICU capacity by treating ICU patients in the post-anaesthesia care unit (PACU) utilizing a collaborative model between an ICU service and a telemedicine service during peak ICU bed demand. Methods We evaluated patients managed by the surgical critical care service in the surgical intensive care unit (SICU) compared to patients managed in the virtual intensive care unit (VICU) located within the PACU. A retrospective review of all patients seen by the surgical critical care service from January 1st 2008 to July 31st 2011 was conducted at an urban, academic, tertiary centre and level 1 trauma centre. Results Compared to the SICU group ( n = 6652), patients in the VICU group ( n = 1037) were slightly older (median age 60 (IQR 47-69) versus 58 (IQR 44-70) years, p = 0.002) and had lower acute physiology and chronic health evaluation (APACHE) II scores (median 10 (IQR 7-14) versus 15 (IQR 11-21), p \u3c 0.001). The average amount of time patients spent in the VICU was 13.7 + /-9.6 hours. In the VICU group, 750 (72%) of patients were able to be transferred directly to the floor; 287 (28%) required subsequent admission to the surgical intensive care unit. All patients in the VICU group were alive upon transfer out of the PACU while mortality in the surgical intensive unit cohort was 5.5%. Discussion A collaborative care model between a surgical critical care service and a telemedicine ICU service may safely provide surge capacity during peak periods of ICU bed demand. The specific patient populations for which this approach is most appropriate merits further investigation